<p>This thesis presents improvements and extensions of a previously presented threat assessment algorithm. The algorithm uses Monte Carlo simulation to find threats in a road scene. It is shown that, by using a wider sample distribution and only apply the most likely samples from the Monte Carlo simulation, for the threat assessment, improved results are obtained. By using this method more realistic paths will be chosen by the simulated vehicles and more complex traffic situations will be adequately handled.</p><p>An improvement of the dynamic model is also suggested, which improves the realism of the Monte Carlo simulations. Using the new dynamic model less false positive and more valid threats are detected.</p><p>A systematic method to choose parameters in a stochastic space, using optimisation, is suggested. More realistic trajectories can be chosen, by applying this method on the parameters that represents the human behaviour, in the threat assessment algorithm.</p><p>A new definition of obstacles in a road scene is suggested, dividing them into two groups, Hard and Soft obstacles. A change to the resampling step, in the Monte Carlo simulation, using the soft and hard obstacles is also suggested.</p>

<p>Dynamic model driven architecture (DMDA) is a architecture made to aid in the</p><p>development of parallel computing code. This thesis is applied to an implementation of</p><p>DMDA known as DMDA3 that should convert graphs of computations into efficient</p><p>computation code, and it deals with the translation of Platform Specific Models (PSM)</p><p>into running systems. Currently DMDA3 can generate schedules of operations but not</p><p>finished code.</p><p>This thesis describes a DMDA3 module that turns a schedule of operations into a</p><p>runable program. Code was obtained from the DMDA3 schedules by reflection and a</p><p>framework was build that allowed generation of low level language code from</p><p>schedules. The module is written in Java and can currently generate C and Fortran code</p><p>for computational tasks. Based on runtime tests for matrix multiplication algorithms the</p><p>generated code is almost as fast as handwritten code.</p><p> </p>

This dissertation describes the development and results of a physiological rigid body forward solution mathematical model that can be used to predict normal knee and total knee arthroplasty (TKA) kinematics and kinetics. The simulated activities include active extension and weight-bearing deep knee bend. The model includes both the patellofemoral and tibiofemoral joints. Geometry of the normal or implanted knee is represented by multivariate polynomials and modeled by constraining the velocity of lateral and medial tibiofemoral and patellofemoral contact points in a direction normal to the geometry surface. Center of mass, ligament and muscle attachment points and normal knee geometry were found using computer aided design (CAD) models built from computer tomography (CT) scans of a single subject. Quadriceps forces were the input for this model and were adjusted using a unique controller to control the rate of flexion, embedded with a controller which stabilizes the patellofemoral joint. The model was developed first using normal knee parameters. Once the normal knee model was validated, different total knee arthroplasty (TKA) designs were virtually implanted. The model was validated using in vivo data obtained through fluoroscopic analysis. In vivo data of the extension and deep knee bend activities from five non-implanted knees were used to validate the normal model kinematics. In vivo kinematic and kinetic data from a telemetric TKA with a tibia component instrumented with strain gauges was used to validate the kinematic and kinetic results of the model implanted with the TKA geometry. The tibiofemoral contact movement matched the trend seen in the in vivo data from the one patient available with this implant. The maximum axial tibiofemoral force calculated with the model was in 3.1% error with the maximum force seen in the in vivo data, and the trend of the contact forces matched well. Several other TKA designs were virtually implanted and analyzed to determine kinematics and bearing surface kinetics. The comparison between the model results and those previously assessed under in vivo conditions validates the effectiveness of the model and proves that it can be used to predict the in vivo kinematic and kinetic behavior of a TKA.

Efficient and reliable public transport systems are fundamental in promoting green growth developments in metropolitan areas. A large range of Advanced Public Transport Systems (APTS) facilitates the design of real-time operations and demand management. The analysis of transit performance requires a dynamic tool that will enable to emulate the dynamic loading of travelers and their interaction with the transit system. BusMezzo, a dynamic transit operations and assignment model was developed to enable the analysis and evaluation of transit performance and level of service under various system conditions and APTS. The model represents the interactions between traffic dynamics, transit operations and traveler decisions. The model was implemented within a mesoscopic traffic simulation model. The different sources of transit operations uncertainty including traffic conditions, vehicle capacities, dwell times, vehicle schedules and service disruptions are modeled explicitly. The dynamic path choice model in BusMezzo considers each traveler as an adaptive decision maker. Travelers’ progress in the transit system consists of successive decisions that are defined by the need to choose the next path element. The evaluations are based on the respective path alternatives and their anticipated downstream attributes. Travel decisions are modeled within the framework of discrete random utility models. A non-compensatory choice-set generation model and the path utility function were estimated based on a web-based survey. BusMezzo enables the analysis and evaluation of proactive control strategies and the impacts of real-time information provision. Several experiments were conducted to analyze transit performance from travelers, operator and drivers perspectives under various holding strategies. This analysis has facilitated the design of a field trial of the most promising strategy. Furthermore, a case study on real-time traveler information systems regarding the next vehicle arrival time investigated the impacts of various levels of coverage and comprehensiveness. As passengers are more informed, passenger loads are subject to more fluctuation due to the traveler adaptations. / QC 20111201

In this thesis, a grid-connected wind-energy converter system including a matrix converter is proposed. The matrix converter, as a power electronic converter, is used to interface the induction generator with the grid and control the wind turbine shaft speed. At a given wind velocity, the mechanical power available from a wind turbine is a function of its shaft speed. Through the matrix converter, the terminal voltage and frequency of the induction generator is controlled, based on a constant V/f strategy, to adjust the turbine shaft speed and accordingly, control the active power injected into the grid to track maximum power for all wind velocities. The power factor at the interface with the grid is also controlled by the matrix converter to either ensure purely active power injection into the grid for optimal utilization of the installed wind turbine capacity or assist in regulation of voltage at the point of connection. Furthermore, the reactive power requirements of the induction generator are satisfied by the matrix converter to avoid use of self-excitation capacitors. The thesis addresses two dynamic models: a comprehensive dynamic model for a matrix converter and an overall dynamical model for the proposed wind turbine system. The developed matrix converter dynamic model is valid for both steady-state and transient analyses, and includes all required functions, i.e., control of the output voltage, output frequency, and input displacement power factor. The model is in the qdo reference frame for the matrix converter input and output voltage and current fundamental components. The validity of this model is confirmed by comparing the results obtained from the developed model and a simplified fundamental-frequency equivalent circuit-based model. In developing the overall dynamic model of the proposed wind turbine system, individual models of the mechanical aerodynamic conversion, drive train, matrix converter, and squirrel-cage induction generator are developed and combined to enable steady-state and transient simulations of the overall system. In addition, the constraint constant V/f strategy is included in the final dynamic model. The model is intended to be useful for controller design purposes. The dynamic behavior of the model is investigated by simulating the response of the overall model to step changes in selected input variables. Moreover, a linearized model of the system is developed at a typical operating point, and stability, controllability, and observability of the system are investigated. Two control design methods are adopted for the design of the closed-loop controller: a state-feedback controller and an output feedback controller. The state-feedback controller is designed based on the Linear Quadratic method. An observer block is used to estimate the states in the state-feedback controller. Two other controllers based on transfer-function techniques and output feedback are developed for the wind turbine system. Finally, a maximum power point tracking method, referred to as mechanical speed-sensorless power signal feedback, is developed for the wind turbine system under study to control the matrix converter control variables in order to capture the maximum wind energy without measuring the wind velocity or the turbine shaft speed.

Today, 3D models of cities are created from aerial images using a camera rig. Images, together with sensor data from the flights, are stored for further processing when building 3D models. However, there is a market demand for a more mobile solution of satisfactory quality. If the camera position can be calculated for each image, there is an existing algorithm available for the creation of 3D models. This master thesis project aims to investigate whether the iPhone 4 offers good enough image and sensor data quality from which 3D models can be created. Calculations on movements and rotations from sensor data forms the foundation of the image processing, and should refine the camera position estimations. The 3D models are built only from image processing since sensor data cannot be used due to poor data accuracy. Because of that, the scaling of the 3D models are unknown and a measurement is needed on the real objects to make scaling possible. Compared to a test algorithm that calculates 3D models from only images, already available at the SBD’s system, the quality of the 3D model in this master thesis project is almost the same or, in some respects, even better when compared with the human eye.

In this thesis, a grid-connected wind-energy converter system including a matrix converter is proposed. The matrix converter, as a power electronic converter, is used to interface the induction generator with the grid and control the wind turbine shaft speed. At a given wind velocity, the mechanical power available from a wind turbine is a function of its shaft speed. Through the matrix converter, the terminal voltage and frequency of the induction generator is controlled, based on a constant V/f strategy, to adjust the turbine shaft speed and accordingly, control the active power injected into the grid to track maximum power for all wind velocities. The power factor at the interface with the grid is also controlled by the matrix converter to either ensure purely active power injection into the grid for optimal utilization of the installed wind turbine capacity or assist in regulation of voltage at the point of connection. Furthermore, the reactive power requirements of the induction generator are satisfied by the matrix converter to avoid use of self-excitation capacitors. The thesis addresses two dynamic models: a comprehensive dynamic model for a matrix converter and an overall dynamical model for the proposed wind turbine system. The developed matrix converter dynamic model is valid for both steady-state and transient analyses, and includes all required functions, i.e., control of the output voltage, output frequency, and input displacement power factor. The model is in the qdo reference frame for the matrix converter input and output voltage and current fundamental components. The validity of this model is confirmed by comparing the results obtained from the developed model and a simplified fundamental-frequency equivalent circuit-based model. In developing the overall dynamic model of the proposed wind turbine system, individual models of the mechanical aerodynamic conversion, drive train, matrix converter, and squirrel-cage induction generator are developed and combined to enable steady-state and transient simulations of the overall system. In addition, the constraint constant V/f strategy is included in the final dynamic model. The model is intended to be useful for controller design purposes. The dynamic behavior of the model is investigated by simulating the response of the overall model to step changes in selected input variables. Moreover, a linearized model of the system is developed at a typical operating point, and stability, controllability, and observability of the system are investigated. Two control design methods are adopted for the design of the closed-loop controller: a state-feedback controller and an output feedback controller. The state-feedback controller is designed based on the Linear Quadratic method. An observer block is used to estimate the states in the state-feedback controller. Two other controllers based on transfer-function techniques and output feedback are developed for the wind turbine system. Finally, a maximum power point tracking method, referred to as mechanical speed-sensorless power signal feedback, is developed for the wind turbine system under study to control the matrix converter control variables in order to capture the maximum wind energy without measuring the wind velocity or the turbine shaft speed.

In markets with cyclical fluctuations, firms may have different dynamic decision rules facing upturns and downturns of industry cycles. This paper extends the dynamic factor demand model to consider industry cycles. Because investment behavior could be endogenous uncertainty involved on industry dynamics, the current industry dynamic models with state-of-the-art would not appropriately interpret industry dynamics. In order to solve the uncertain problem, we utilize the idea of transfer probability in Markov switching model to catch the industry cyclical behavior. Explicitly incorporating the Markov regime switching mechanism based on Nelson and Kim (2000), this paper measures the firm¡¦s dynamic adjustments when facing upturns and downturns of industry cycles. The empirical work is based on firm level data of Taiwan high-technology industries. The empirical results show that the expansionary strategy in labor and capital usage may not have positive impacts on output when considering uncertainty that may be casued by business cycles. To have correct prediction in cyclical fluctuation becomes important task for high-technology firms. However, the positive contribution of exogenous technology to output growth is so significant. This proves why every industry tries to impel technology in recent years. The industry dynamic model integrated with cyclical fluctuation and demand uncertainty allows us to examine how sharp changes in financial factors might affect investment behavior, technological nature and adjustment effects for industries in facing demand and investment shocks.

<p>In this work a particle filter (PF) that uses a one-dimensional dynamic model to estimate the position of vehicles traveling on a road is derived. The dynamic model used in the PF is a second order linear-Gaussian model. To be able to track targets traveling both on and off road two different multiple model filters are proposed. One of the filters is a modified version of the Efficient Interacting Multiple Model (E-IMM) and the other is a version of the Multiple Likelihood Models (MLM). Both of the filters uses two modes, one for the on road motion and one for the off road motion. The E-IMM filter and the MLM filter are compared to the standard PF to be able to see the performance gain in using multiple models. This result indicates that the multiple model filters have better performance, at least when the true mode switching probabilities are used.</p> / <p>Den här arbetet presenterar ett partikelfilter som använder sig av en endimensionell dynamisk modell för att skatta positionen på fordon som befinner sig på någon väg. Den dynamiska modellen som används i partikelfiltret är en andra ordningens linjär-gaussisk modell. För att kunna spåra fordon som befinner sig både på och utanför vägen så föreslås två olika multipla filter. Ena filtret är en modifierad</p><p>variant av Efficient Interacting Multiple Model (E-IMM) och den andra är en version a Multiple Likelihood Models (MLM). Båda filtren använder sig av två moder, en för rörelse på vägen och en för rörelse utanför vägen. E-IMM filtret och MLM filtret jämförs med ett standard partikelfilter för att kunna se förbättringen vid använding av multipla modeller. Resultatet visar att båda multipla modell filtren ger bättre resultat, i varje fall då rätt sannolikheter för modbyte används.</p>

Analysis of circular data becomes more and more popular in many fields of studies. In this report, I present two statistical analysis of circular data using von Mises distributions. Firstly, the maximization-expectation algorithm is reviewed and used to classify and estimate circular data from the mixture of von Mises distributions. Secondly, Forward Filtering Backward Smoothing method via particle filtering is reviewed and implemented when circular data appears in the dynamic state-space models. / text